Eugenio Brentari

Hedonic Price for the Italian Red Wine: A Panel Analysis

We use a unique dataset to estimate the hedonic price function for Italian red wine sold on the Italian market in the period 2006-2008. For each bottle considered, the dataset allows us to know several characteristic such as the price by retail channel (price in supermarkets and in wine shops), label characteristics, chemical analysis, sensorial characteristics and experts’ evaluations. The objective of the analysis is to estimate the importance of the different class of variables on price and through time. The analysis shows that the price mainly depends on the label characteristics of the wine sold; other indicators, even when statistically significant, are quite irrelevant. The producer seems to have a limited market power especially in the large distribution where label characteristics explain about 90% of price formation. For wine shops the single characteristics of each wine seem to be more relevant.

Clustering ranking data in market segmentation: a case study on the Italian McDonald's customers’ preferences†

ABSTRACT Cluster analysis is often used for market segmentation. When the inputs in the clustering algorithm are ranking data, the intersubject (dis)similarities must be measured by matching-type measures, able to take account of the ordinal nature of the data. Among them, we used a Weighted Spearmans rho, suitably transformed into a (dis)similarity measure, in order to emphasize the concordance on the top ranks. This allows creating clusters grouping customers that place the same items (products, services, etc.) higher in their rankings. Also the statistical instruments used to interpret the clusters must be conceived to deal with ordinal data. The median and other location measures are appropriate but not always able to clearly differentiate groups. The so-called bipolar mean, with its related variability measure, may reveal some additional features. A case study on real data from a survey carried out in the Italian McDonalds restaurants is presented.

Osservazioni sull'intervallo di confidenza dell'indice di concentrazione di Gini, proposto da Gastwirth et al.

This article points out that the interval of confidence for the Gini concentration ratio —proposed by Gastwirth et al.—is an asymptotically ultra-conservative confidence interval. It has in fact been shown the probability of the confidence interval tends asymptotically to one. This result has been empirically checked by using the methodology based on the simulation of the sample space. The simulations carried out on three models for income distribution that is Pareto, Log-normal and Dagum, allowed us to remark that the number of samples required to verify the asymptotic convergence increases with the increase of the number of income classes into which the sample is divided. The number of samples in which the confidence level of the interval examined was equal to one is considerably lower than the numbers usually considered in real analysis of income distribution.